The Al/SiC composites have received more commercial attention than other kinds of Metal Matrix Composites (MMCs) due to their high performance. However, a continuing problem with MMCs is that they are difficult to machine, due to the hardness and abrasive nature of the SiC particles. Grinding is often the method of choice for machining Al/SiC composites to acquire high dimensional accuracy and surface finish in large scale production. Based on the full factorial design (34), a total of 81 experiments, each having a combination of different levels of variables, are carried out to study the effect of grinding parameters such as wheel velocity, work piece velocity, feed and depth of cut on the responses such as tangential grinding force, roughness and grinding temperature. Modeling and optimization place a vital role in controlling any process for improved product quality, high productivity and low cost. In the present work, experimental results are used to calculate the analysis of variance (ANOVA) which explains the significance of the parameters on the responses. Based on the results of ANOVA, a mathematical model is formulated using multiple regression method. A genetic algorithm (GA) based optimization procedure has been developed to optimize the grinding parameters for maximum material removal by imposing constraints on roughness. This methodology would be useful for identifying the optimum grinding parameters in order to achieve the required material removal rate (MRR).